This article focuses on conditional generative modeling (CGM) for image data with continuous, scalar conditions (termed regression labels). We propose the first model for this task which is called continuous conditional generative adversarial network (CcGAN). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels). Conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; and (P2) since regression labels are scalar and infinitely many, conventional label input mechanisms (e.g., combining a hidden map of the generator/discriminator with a one-hot encoded label) are not applicable. We solve these problems by: (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a naive label input (NLI) mechanism and an improved label input (ILI) mechanism to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. Hence, we propose four versions of CcGAN employing different proposed losses and label input mechanisms. The error bounds of the discriminator trained with HVDL and SVDL, respectively, are derived under mild assumptions. To evaluate the performance of CcGANs, two new benchmark datasets (RC-49 and Cell-200) are created. A novel evaluation metric (Sliding Fréchet Inception Distance) is also proposed to replace Intra-FID when Intra-FID is not applicable. Our extensive experiments on several benchmark datasets (i.e., RC-49, UTKFace, Cell-200, and Steering Angle with both low and high resolutions) support the following findings: the proposed CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label; and CcGAN substantially outperforms cGAN both visually and quantitatively.

CSin3 is a cohort-based, three-year computer science bachelor's degree program that has increased graduation rates of traditionally underrepresented computer science students. A collaborative effort between a community college and a public university, CSin3 provides a clear pathway for upward socio-economic mobility into the high-paying technology industry. CSin3 students are 90% from traditionally underrepresented groups, 80% first-generation, 32% female, and have a three-year graduation rate of 71%, compared to a 22% four-year graduation rate for traditional computer science students. Upon graduation, CSin3 students score similarly on a standardized exam of computer science knowledge as compared to traditional students who graduate in 4 years or more. The first graduates had a job placement rate of 78% within two months of graduation, including positions at large technology companies like Apple, Salesforce, and Uber. By implementing a cohort-based learning community, a pre-defined course pathway, just-in-time academic and administrative support, comprehensive financial aid, and a focus on 21st century skills, the CSin3 program has demonstrated promising results in addressing the capacity, cost, quality, and diversity challenges present in the technology industry.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide--adenine (A), thymine (T), cytosine (C) or guanine (G)--is altered. Arguably, SNPs account for more than 90% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger-based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system (known as SNP Chart) is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias. RESULTS: Using a set of 32 Coriell DNA samples plus three negative PCR controls as a training data set, we have developed a fully-automated genotyping algorithm based on simple linear discriminant analysis (LDA) using dynamic variable selection. The algorithm combines separate analyses based on the multiple probe sets to give a final posterior probability for each candidate genotype. We have tested our algorithm on a completely independent data set of 270 DNA samples, with validated genotypes, from patients admitted to the intensive care unit (ICU) of St. Paul's Hospital (plus one negative PCR control sample). Our method achieves a concordance rate of 98.9% with a 99.6% call rate for a set of 96 SNPs. By adjusting the threshold value for the final posterior probability of the called genotype, the call rate reduces to 94.9% with a higher concordance rate of 99.6%. We also reversed the two independent data sets in their training and testing roles, achieving a concordance rate up to 99.8%. CONCLUSION: The strength of this APEX chemistry-based platform is its unique redundancy having multiple probes for a single SNP. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any 'bad data' corresponding to image artifacts on the microarray slide or failure of a specific chemistry. In this regard, our method is able to automatically select the probes which work well and reduce the effect of other so-called bad performing probes in a sample-specific manner, for any number of SNPs.

The authors describe a sequential strategy for designing manufacturable integrated circuits using available CAD tools. Optimizing the performance of complex designs in the presence of unwanted parameter variations can take a prohibitively large number of computer runs. These methods overcome this complexity by combining sequential experimentation with modeling of the CAD simulator output as realizations of stochastic processes. These models give inexpensive approximations that enable location of subregions on which to continue the search for the optimal design. The multistage experimentation reduces the number of simulation runs required to obtain sufficiently accurate approximations. The strategy is able to treat circuits with many parameters, performances, and criteria. Included are two examples where improved designs have been found by these methods. The first is a zero-temperature-coefficient current reference IC. The second is a gallium arsenide voltage level shifter IC with multiple performances of interest.>

A method for parametric yield optimization which significantly reduces the simulation cost is proposed. The method assumes that the circuit performances ultimately determining yield can be approximated by computationally inexpensive functions of the inputs to the circuit simulator. These inputs are the designable parameters, the uncontrollable statistical variations, and the operating conditions of interest. The authors fit the approximating functions to data from a statistical experiment incurring relatively few runs of the simulator. For a given set of designable parameters, the fitted models predict the circuit performances as the uncontrollable parameters and operating conditions vary. The predictions lead to estimates of the parametric yield, which is then numerically maximized with respect to the designable parameters. The authors give a CMOS circuit example where sufficiently accurate predictions and good actual yield result from about 100 runs of the circuit simulator.>